The Association Between the Legal and Financial Reporting Environments and Forecast Performance of Individual Analysts

The Association Between the Legal and Financial Reporting Environments and Forecast Performance of Individual Analysts Ran Barniv Kent State University Graduate School of Management, CBA Kent, OH 44242 Phone: 330-672-1112 Fax: 330-672-2545 E-mail: rbarniv@bsa3.kent.edu Mark Myring Ball State University College of Business Muncie, IN 47306 Phone: 765-285-5108 Fax: 765-285-8024 E-mail: mmyring@bsu.edu Wayne B Thomas* University of Oklahoma Michael F Price College of Business 307 W Brooks, Room 212B Norman, OK 73019 Phone: 405-325-5789 Fax: 405-325-7348 E-mail: wthomas@ou.edu * Contact author We appreciate the helpful comments of two anonymous reviewers, Patricia O’Brien (associate editor), A Amir, S Beninga, workshop participants at the universities of Cincinnati, Pennsylvania State, and Tel Aviv, and participants in a concurrent session of the AAA annual meeting, Hawaii, August 2003 We also acknowledge Thomson Financial for providing I/B/E/S International and U.S Detail History data The Association Between the Legal and Financial Reporting Environments and Forecast Performance of Individual Analysts Abstract We test the ability of analyst characteristics to explain relative forecast accuracy across legal origins (common law versus civil law) Common law countries generally have more effective corporate governance mechanisms, including stronger investor protection laws and inputs provided through higher-quality financial reporting systems In this type of environment, we predict that analysts with superior ability and resources in common law countries will more consistently outperform their peers because appropriate market-based incentives exist In civil law countries, where the demand for earnings information is reduced because of weaker corporate governance mechanisms and lower-quality financial reporting, we predict that analysts with superior ability will less consistently provide superior forecasts Results are consistent with our expectations and suggest an association between legal and financial reporting environments and analysts’ forecast behavior Keywords Analysts’ characteristics, relative forecast performance, common law, civil law JEL Descriptors G38, K22, M41 The Association Between the Legal and Financial Reporting Environments and Forecast Performance of Individual Analysts Introduction We test the ability of analyst characteristics to explain relative forecast accuracy across legal origins In common law countries, where investor protection laws are stronger and financial reporting is generally perceived to have higher quality (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998, 2000a; Ball, Kothari, and Robin 2000), the increased demand by investors for earnings information may create incentives for analysts to provide that information accurately Analysts with superior characteristics (e.g., ability, effort, experience, resources, etc.) are more likely to issue a superior forecast relative to their peers In civil law countries, weaker investor protection laws and lower-quality financial reporting may reduce the economic incentives of analysts to incur costly activities to provide a superior earnings forecast We expect that it will be more difficult to relate individual analysts’ characteristics to relative forecast performance in civil law countries Examining the relation between relative forecast performance and analyst characteristics across legal regimes provides evidence outside the United States, where the bulk of this research has been conducted.1 Understanding analyst behavior in other environments provides additional insight into how analysts’ efforts in accurately forecasting earnings can contribute to the informational efficiency of financial markets (Frankel, Kothari, and Weber 2002) The results also contribute to our understanding of the relation between investors’ demands and analysts’ behavior (Defond and Hung 2002) As the value relevance of reported earnings declines, investors may have less demand for analysts’ earnings forecasts and demand other sources of information such as cash flow forecasts Our results may also be helpful in investigating other related research issues, such as the value relevance of accounting numbers across countries Prior research has focused primarily on estimating the relation between earnings and stock prices to understand investors’ demand for accounting earnings (e.g., Ball, Kothari, and Robin 2000; Ali and Hwang 2000) We extend this literature by examining whether the relation between analyst characteristics and relative forecast accuracy differs across legal origins consistent with investors’ demand for earnings information Consistent with expectations, we find that the relation between analyst characteristics and relative forecast accuracy is stronger in common law countries These results are consistent with analysts’ forecast behavior responding to the demand by investors for earnings information In common law countries where investor protection laws are stronger, financial reporting is higher-quality, and the demand by investors for earnings information is greater, analysts with superior abilities tend to distinguish themselves more clearly In civil law countries, it is more difficult to explain analysts’ relative forecast accuracy Overall, we find that the relation is strongest in the United States, followed by non-U.S common law countries The relation is weakest in the civil law countries Results within the three origins of the civil law classification (French, German, and Scandinavian) suggest that the quality of financial reporting systems plays a role in these relations beyond the influence of investor protection laws Finally, we find some empirical support for the notion that cash flow forecasts may substitute for earnings forecasts when earnings are less relevant (Defond and Hung 2002) The relation between analysts’ characteristics and relative cash flow forecast accuracy is stronger in civil law countries than in common law countries The remainder of the paper is organized as follows Section develops the hypotheses Section outlines the research design and section details the data and sample selection Section reports results and section provides additional analyses The paper concludes in section Hypotheses We provide the following rationale for our tests Common law countries are generally perceived to have stronger investor protection laws (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998, 2000a)3 and higher-quality financial reporting (Ball, Kothari, and Robin 2000).4 In these settings, earnings information can play a more prominent role in corporate governance mechanisms and therefore have greater value relevance.5 The greater value relevance of earnings information increases investors’ demand for that information when making decisions The increased demand by investors offers proper economic incentives for analysts to compete in providing accurate forecasts of earnings Those analysts having the ability and resources to outperform other analysts will, on average, so because the market-based reward structure established by investor demand offers analysts fair incentives (Schipper 1991) In other words, the rewards for making accurate forecasts fairly outweigh the cost of gathering and processing information when investor protection laws are strong and the quality of the financial reporting system is good For common law countries, we expect analysts with superior characteristics (ability, effort, experience, resources, etc.) to more consistently outperform their peers, resulting in a stronger relation between analysts’ characteristics and relative forecast accuracy In civil law countries, financial accounting systems are generally perceived to be of lower quality in terms of their ability to reflect accurately the underlying economic activity of the firm (Ball, Kothari, and Robin 2000; Guenther and Young 2000; Bhattacharya, Daouk, and Welker 2003; Francis, Khurana, and Pereira 2004) Financial accounting practices in civil law countries are oriented less toward serving the needs of outside investors (O’Brien 1998; Lang, Lins, and Miller 2004) and investor protection laws are weaker (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998, 2000a) These factors likely weaken the demand by investors for earnings information, which reduces the economic incentives of superior analysts to outperform their peers Providing a superior earnings forecast is costly and analysts with superior abilities and resources will incur the incremental costs of gathering information only when they expect to be equitably rewarded We expect that the reduction in incentives of superior analysts to make superior forecasts will lead to a weaker relation between analyst characteristics and relative forecast performance in civil law countries (i.e., relative forecast accuracy occurs more randomly in civil law countries) Furthermore, among the common law countries, prior studies cited in the preceding paragraphs suggest that the United States has some of the strongest investor protection laws and higher-quality financial reporting If investor protection laws and quality of financial reporting affect the relevance of accounting earnings to investors, one would expect the demand for earnings information by investors and the incentives of analysts to compete and provide that information accurately to be greater in the United States than in most of the non-U.S common law countries Similarly, as discussed in the preceding paragraphs, we expect that analysts in non-U.S common law countries will have more incentive to compete and provide more accurate forecasts relative to their peers than will analysts in civil law countries Overall, the preceding ideas lead to our first three hypotheses H1: Analyst characteristics better explain relative forecast accuracy in the common law countries than in civil law countries H2: Analyst characteristics better explain relative forecast accuracy in the United States than in non-U.S common law countries H3: Analyst characteristics better explain relative forecast accuracy in non-U.S common law countries than in civil law countries It is also interesting to consider whether the strength of investor protection laws or the quality of the financial reporting system offers the greater motivation to analysts to provide superior forecasts We examine whether analyst characteristics are useful for explaining relative forecast accuracy across three groups of civil law countries Within the civil law origin, La Porta, Lopez-deSilanes, Shleifer, and Vishny (1997, 1998) find that countries of the French origin have weaker investor protection laws than countries of the German origin However, countries of the French origin have higher quality and more transparent financial accounting information than countries of the German origin (Ball, Kothari, and Robin 2000; Francis, Khurana, and Pereira 2004) Thus, the incentives for analysts to provide superior forecasts might be stronger in the German origin countries because of better investor protection laws or stronger in the French origin countries because of higher-quality financial reporting By estimating the ability of analyst characteristics to explain relative forecast accuracy in the French versus German origins, we expect to obtain some indication of the impact that investor protection laws, on the one hand, versus quality of financial reporting, on the other hand, has on the behavior of analysts Relative to other civil law origin countries, the Scandinavian origin has the better investor protection laws (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998) and the higher-quality financial reporting (Ball, Kothari, and Robin 2000; Francis, Khurana, and Pereira 2004) We therefore predict that analyst characteristics will have greater explanatory power for this group of civil law countries Thus, H4: Analyst characteristics differentially explain the relative forecast accuracy across the three civil law origins Table summarizes the strength of investor protection laws and the quality and transparency of financial reporting and their expected impact on the relative performance of analysts across legal origins Research design and determinants of relative forecast accuracy Extending the model of Jacob, Lys, and Neale (1999), we examine the impact of analyst activity, experience, portfolio complexity, specialization, and internal environmental factors on the ability of analysts to produce superior forecasts of earnings relative to their peers One possible limitation of using their model is that it was developed in a U.S context While there could be other important analyst characteristics in other countries, we believe the United States provides a good setting for establishing a benchmark model of the way in which analyst characteristics explain relative forecast accuracy when the demand for earnings information is high.6 We estimate the following model, where the first ten variables are those used in Jacob, Lys, and Neale (1999) and the final three represent additional international attributes of analysts and their brokerage firms (AFEk,j,t/MAFEj,t)-1 = α0 + β1*HORIZk,j,t + β2*CHANGEk,j,t + β3*EXPk,j,t + β4*COMPk,j,t + β5*SPECk,j,t + β6*FREQk,j,t + β7*B-SIZEk,j,t + β8*B-INDk,j,t + β9*PINk,j,t + β10*POUTk,j,t + β11*C-EXPk,j,t + β12*C-SPECk,j,t + β13*B-Ck,j,t + εk,j,t The dependent variable measures the relative forecast accuracy of analyst k to all other analysts following company j in year t AFE is the absolute value of analyst k’s forecast error and MAFE is the mean absolute forecast error of all analysts issuing a forecast for company j in year t.7 The independent variables are defined as follows: HORIZ = The number of calendar days between the forecast issue date and the earnings announcement date CHANGE = Dummy variable that takes a value of (0 otherwise) when there 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Financial Studies (1): 125-148 31 TABLE Investor protection laws, quality and transparency of financial reporting, and their expected impact on the ability of analyst characteristics to explain relative forecast accuracy Expected Impact on Relative Forecasts Quality and Transparency of Accuracy Origin Investor Protection Laws Financial Reporting Efficiency Antidirector Creditor of Judicial Accrual Disclosure Audit Rightsa Rightsa Systemb Indexc Indexd Spendinge Common Law f Stronger 4.0 3.11 8.15 Higher 0.76 70.6 0.27 Greater Civil Lawg Average 2.4 1.83 7.39 Average 0.58 65.1 0.14 Less United States Non-U.S Common Frenchh Germani Scandinavianj Strongest Stronger 5.0 3.9 1.00 3.23 10.0 8.04 Highest Higher 0.86 0.74 71.0 70.5 0.24 0.27 Greatest Greater Weaker Average Above Avg 2.3 2.3 3.0 1.58 2.33 2.00 6.56 8.54 10.0 Average Below Avg Higher 0.64 0.43 0.63 62.1 62.7 74.0 0.12 0.12 0.22 Below average Below average Average Numbers reported represent mean amounts obtained from the following studies: a La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) – higher number indicates more rights, creditor rights may be classified separately b La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) – higher number indicates more efficiency c Hung (2000) – higher number indicates greater use of accruals d Francis, Khurana, and Pereira (2004) – higher number indicates better disclosure e Jaffe (1992) as reported in Francis, Khurana, and Pereira (2004) – higher number indicates more audit spending f Countries in the common law origin are Australia, Canada, Hong Kong, India, Ireland, Malaysia, New Zealand, Singapore, South Africa, Thailand, United Kingdom, and United States g Countries in the civil law origin are Argentina, Austria, Belgium, Denmark, Finland, France, Germany, Indonesia, Italy, Japan, South Korea, Mexico, Netherlands, Norway, Philippines, Portugal, Spain, Sweden, Switzerland, Taiwan, and Turkey h Countries in the French origin are Argentina, Belgium, France, Indonesia, Italy, Mexico, Netherlands, Philippines, Portugal, Spain, and Turkey i Countries in the German origin are Austria, Germany, Japan, South Korea, Switzerland, and Taiwan j Countries in the Scandinavian origin are Denmark, Finland, Norway, and Sweden 32 TABLE I/B/E/S summary statistics for our sample, sample period 1984-2001 Panel A: Number of analysts and forecasts across legal origins Number of Analystsa Legal Origin Common law Origind Civil law Origine Final Observations Used Number of Firm-Year Forecastsb Number of Firm yearsc Average Number of Analysts Following 18,009 9,073 27,082 535,462 138,355 673,817 53,785 16,806 70,591 9.96 8.23 US Non-U.S common law Origin 9,198 8,811 390,121 145,341 36,985 16,800 10.55 8.66 French Originf German Origing Scandinavian Originh 4,735 3,129 1,209 70,011 52,951 15,393 7,488 7,469 1,849 9.35 7.09 8.33 Panel B: Firm characteristics across legal origins Common Law Origin Civil Law Origin U.S Non-U.S Common Law Origin MVE Mean(Med) 1004.10 (444.61) 1199.18 (643.13) 1060.78 (486.33) 879.38 (365.57) EPS/PRICE Mean(Med) 0.0905 (0.061) 0.0487 (0.005) 0.0318 (0.0554) 0.2209 (0.0814) SD(CON)/PRICE Mean(Med) 0.0168 (0.002) 0.0177 (0.001) 0.0043 (0.0010) 0.0446 (0.0094) AFE/PRICE Mean(Med) 0.0706 (0.1620) 0.0310 (0.001) 0.0666 (0.0430) 0.0795 (0.2188) 0.0873 (0.0134) 0.0131 (0.0034) 0.0360 (0.0530) 0.0305 (0.0028) 0.0042 (0.0004) 0.0205 (0.0082) 0.0565 (0.0022) 0.0076 (0.0006) 0.0301 (0.0068) 18.1* 77.3* -1.8 9.7* 27.9* 122.6* -79.5* -62.9* -89.0* -133.0* -8.7* -78.7* French Origin 965.15 (419.17) German Origin 1540.25 (1102.75) Scandinavian Origin 786.78 (378.32) Common-Law Origin versus Civil-Law Origin t-statastic -18.7* Wilcoxon -18.9* U.S versus non-U.S Common-Law Origin t-statastic 17.0* Wilcoxon 17.0* (Table continued on the next page) 33 TABLE (continued) I/B/E/S summary statistics for our sample, sample period 1984-2001 Panel B (continued): Firm characteristics across legal origins Non-U.S Common-Law Origin versus Civil-Law Origin t-statastic -25.3* 25.3* Wilcoxon -25.2* 84.6* French Origin versus German Origin t-statastic -28.8* 20.5* Wilcoxon -33.6* 20.4* French Origin versus Scandinavian Origin t-statastic 6.2* 7.1* Wilcoxon 1.3 -13.0* German Origin versus Scandinavian Origin t-statastic 23.9* -8.8* Wilcoxon 25.2* -35.4* 52.1* 80.9* 24.1* 65.8* 27.4* 26.6* 21.0* 24.2* 5.1* -21.3* 5.6* -15.4* -24.4* -46.6* -13.7* -38.0* a The number of analysts includes all analysts that provide at least one forecast for one company during the period Many analysts provide forecasts for many companies b Only the most recent forecast is included in the reported number of firm-year forecasts c We report only forecasts for companies followed by at least three analysts d Common law countries are Australia, Canada, Hong Kong, India, Ireland, Malaysia, New Zealand, Singapore, South Africa, Thailand, United Kingdom, and United States e Civil law countries are Argentina, Austria, Belgium, Denmark, Finland, France, Germany, Indonesia, Italy, Japan, South Korea, Mexico, Netherlands, Norway, Philippines, Portugal, Spain, Sweden, Switzerland, Taiwan, and Turkey f Countries in the French origin are Argentina, Belgium, France, Indonesia, Italy, Mexico, Netherlands, Philippines, Portugal, Spain, and Turkey g Countries in the German origin are Austria, Germany, Japan, South Korea, Switzerland, and Taiwan h Countries in the Scandinavian origin are Denmark, Finland, Norway, and Sweden MVE = Market value of equity in million U.S dollars EPS/PRICE = EPS deflated by price (including negative EPS) SD(CON)/PRICE = Standard deviation of I/B/E/S consensus deflated by price AFE/PRICE = Absolute forecast error deflated by price TABLE Raw univariate statistics: Means (medians) by origin, sample period: 1984-2001a Panel A: Common law origin, civil law origin, United States, and non-U.S common law origin U.S vs Non-U.S Non-U.S Common Non-U.S Common Common Civil Common vs Civil common vs Civil Law Law United Law t-stat t- stat t-stat b c d e e Variables Origin Origin States Origin (Z- stat.) (Z- stat.) (Z-stat.)e HORIZ 123.3 134.6 124.1 121.3 -38.3 * 9.44* -31.9* (95.0) (111.0) (95.0) (93.0) (-27.6) * (23.8)* (-29.8)* 34 CHANGE EXP COMP SPEC FREQ B-SIZE B-IND PIN POUT C-EXP C-SPEC B-C 0.20 (0.00) 0.80 (0.69) 25.2 (17.0) 0.67 (0.80) 3.56 (3.00) 0.87 (0.94) 0.13 (0.09) 0.29 (0.25) 0.25 (0.21) 0.56 (1.00) 0.93 (1.00) 0.83 (1.00) 0.21 (0.00) 0.59 (0.69) 26.2 (14.0) 0.51 (0.50) 2.96 (2.00) 0.84 (0.90) 0.12 (0.09) 0.38 (0.33) 0.29 (0.24) 0.31 (0.00) 0.78 (1.00) 0.60 (0.67) 0.24 (0.00) 0.84 (0.69) 22.3 (18.0) 0.71 (0.85) 3.11 (3.00) 0.88 (0.94) 0.14 (0.10) 0.27 (0.24) 0.24 (0.20) 0.61 (1.00) 0.98 (1.00) 0.91 (1.00) 0.19 (0.00) 0.71 (0.69) 32.9 (16.0) 0.58 (0.64) 4.78 (3.00) 0.87 (0.91) 0.10 (0.08) 0.33 (0.27) 0.29 (0.23) 0.42 (0.00) 0.79 (1.00) 0.64 (0.68) (TABLE continued on next page) 35 -4.90 * (-4.93) * 106.1 * (88.6) * -9.17 * (90.7) * 174.2 * (166.3) * 65.6 * (67.4) * 68.9 * (67.4) * 28.5 * (8.23) * -133.2 * (-136.7) * -65.9 * (-61.6) * 177.5 * (167.0) * 113.4 * (105.2) * 205.3 * (171.2) * 12.5* (12.4)* 58.3* (48.5)* -85.6* (30.4)* 133.7* (141.9)* -113.0* (-39.6)* 20.0* (48.1)* 127.0* (90.6)* -88.5* (-69.2)* -81.7* (-75.4)* 170.5* (166.9)* 224.1* (54.8)* 289.2* (273.6)* -11.4* (-11.4)* 48.6* (45.6)* 36.2* (47.7)* 51.8* (48.9)* 104.2* (82.2)* 18.7* (10.6)* -41.1* (-32.0)* -46.0* (-52.4)* -6.16* (-9.20)* 61.4* (60.9)* 6.30* (23.6)* 27.6* (16.6)* TABLE (continued) Raw univariate statistics: Means (medians) by origin, sample period: 1984-2001a Panel B: Civil Law Origin – French, German, and Scandinavian French vs German French German Scand t- stat b f g h Variables Origin Origin Origin (Z- stat.)e HORIZ 132.7 139.7 127.4 -10.8 * (111.0) (118.0) (99.0) (-12.1)* CHANGE 0.20 0.22 0.21 -9.50 * (0.00) (0.00) (0.00) (-9.50) * EXP 0.59 0.60 0.54 -3.64 * (0.69) (0.69) (0.69) (-1.71) COMP 27.7 27.6 19.3 0.87 (14.0) (15.0) (10.0) -(5.43)* SPEC 0.47 0.56 0.52 -42.4 * (0.40) (0.57) (0.50) (-41.9)* FREQ 3.04 2.94 3.07 4.79 * (2.00) (2.00) (2.00) (16.6) * B-SIZE 0.84 0.86 0.84 -15.6 * (0.89) (0.90) (0.92) (-3.38)* B-IND 0.10 0.11 0.17 -3.41 * (0.08) (0.08) (0.11) (-0.98) PIN 0.39 0.33 0.38 43.1 * (0.34) (0.27) (0.35) (44.9) * POUT 0.28 0.28 0.27 0.07 (0.23) (0.22) (0.25) (4.71)* C-EXP 0.29 0.36 0.25 -25.7 * (0.00) (0.00) (0.00) (-25.6) * C-SPEC 0.77 0.82 0.66 -23.2 * (1.00) (1.00) (0.86) (-29.4) * B-C 0.59 0.69 0.42 -40.8 * (0.59) (0.94) (0.29) (-33.2)* a French vs Scand t- stat (Z- stat.)e 5.65* (5.85)* -3.25* (-3.30)* 9.90* (9.29)* 28.2* (37.1)* -16.2* (-18.8)* -1.33 (-10.0)* -0.67 (-2.64)* -49.6* (-38.0)* 2.45** (0.53) 14.9* (-4.11)* 10.1* (10.2)* 10.4* (37.3)* 53.7* (45.6)* German vs Scand t- stat (Z- stat.)e 12.9 * (13.7) * 2.79 * (2.76) * 12.1 * (9.84) * 26.8 * (40.0) * 10.3 * (13.0) * -5.09 * (-20.9) * -10.2 * (-0.14) -63.1 * (-36.4) * -27.0 * (31.7) * 6.61 * (-8.41) * 27.0 * (25.4)* 47.3 * (57.3) * 81.4 * (74.3) * Unadjusted raw amounts for mean and median are reported since subtracting annual firm-year means for each variable produces means equal to zero b Independent variables: HORIZ = The number of calendar days between the forecast issue date and the earnings announcement date CHANGE = Dummy variable that takes a value (0 otherwise) when there has been a change in the assignment of specific analyst k following company j for a particular brokerage in year t EXP = The natural log of the number of years analyst k has issued forecasts for company j COMP = The number of companies followed by analyst k in the calendar year in which the forecast was issued SPEC = Percentage of companies followed by analyst k with the same I/B/E/S industry code as company j (1.00= 100%) FREQ = Number of forecasts issued by analyst k for company j in year t 36 B-SIZE = B-IND = PIN = POUT = C-EXP = C-SPEC = B-C = Percentile ranking of the total number of analysts employed by the brokerage house to which analyst k belongs in the calendar year in which the forecast was issued, relative to other brokerage houses (1.00= 100%) Percentage of analyst k’s brokerage house analysts which follows company j’s industry in the calendar year in which the forecast was issued (1.00= 100%) Portion of new analysts that come from outside the brokerage house relative to the total number of analysts who worked for analysts k’s brokerage house during the calendar year in which the forecast was issued (1.00= 100%) Portion of analysts who left analyst k’s brokerage house relative to the total number of analysts who worked for analysts k’s brokerage house during the calendar year in which the forecast was issued (1.00= 100%) Dummy variable that takes a value of (0 otherwise) when analyst k has issued forecasts for more than three years for any company in a country Percentage of companies followed by analyst k in the same country where the analyst has issued forecasts for company j in year t (1.00= 100%) Percentage of analyst k’s brokerage house analysts which follow company j’s country in the calendar year in which the forecast was issued (1.00= 100%) c Countries in the common law origin are Australia, Canada, Hong Kong, India, Ireland, Malaysia, New Zealand, Singapore, South Africa, Thailand, United Kingdom, and the United States d Countries in the civil law origin are Argentina, Austria, Belgium, Denmark, Finland, France, Germany, Indonesia, Italy, Japan, South Korea, Mexico, Netherlands, Norway, Philippines, Portugal, Spain, Sweden, Switzerland, Taiwan, and Turkey e Amounts shown represent tests of differences in means (medians) between the two groups f Countries in the French origin are Argentina, Belgium, France, Indonesia, Italy, Mexico, Netherlands, Philippines, Portugal, Spain, and Turkey g Countries in the German origin are Austria, Germany, Japan, South Korea, Switzerland, and Taiwan h Countries in the Scandinavian origin are Denmark, Finland, Norway, and Sweden *, ** Significant at p < 0.01, 0.05 37 TABLE Regression of analyst relative forecast accuracy on analyst- broker-specific characteristics, sample period: 1984-2001 U.S vs Non-U.S Non-U.S Common Non-U.S Common vs Common Civil Common vs Civil Common Civil Predicted Law Law United Law t-stat.d t-stat.d t-stat.d Variablesa Sign Originb Originc States Origin H1: H2: H3: Intercept -0.0019 -0.0030 0.0001 -0.0056 0.30 1.30 -0.55 (-0.91) (-0.99) (0.04) (-0.87) HORIZ + CHANGE -/? EXP - COMP + SPEC -/? FREQ - B-SIZE - B-IND - PIN + POUT + C-EXP - C-SPEC - B-C - Chow-F e R2 Obs 0.0037 0.0023 (170.2)* (78.4)* -0.0132 0.0018 (-4.17)* (0.31) -0.0037 0.0038 (-1.39) (0.73) 0.0002 -0.0001 (2.88)* (-1.13) -0.0551 0.0076 (-7.86)* (0.72) -0.0397 -0.0156 (-51.3)* (-13.5)* -0.220 -0.040 (-18.6)* (-1.87) -0.0318 0.1760 (-2.16)** (4.60)* 0.0420 0.0367 (4.88)* (3.17)* 0.1385 0.0888 (15.7)* (7.42)* 0.0082 0.0086 (2.92)* (1.55) -0.0418 -0.0239 (-3.72)* (-2.58)** -0.0734 -0.0784 (-11.3)* (-8.63)* 0.0038 0.0031 (149.3)* (82.0)* -0.0227 0.0119 (-6.23)* (1.86) 0.0011 0.0047 (0.35) (0.79) 0.0007 -0.0004 (6.18)* (-2.95)* -0.0620 -0.0260 (-7.39)* (-1.96)** -0.0535 -0.0356 (-54.5)* (-33.2)* -0.230 -0.080 (-17.8)* (-2.99)* -0.0250 -0.0218 (-1.60) (-0.44) 0.0296 0.0512 (2.76)* (3.50)* 0.1632 0.0920 (14.7)* (6.48)* 0.0076 0.0077 (2.27)** (1.39) -0.0636 -0.0402 (-2.63)* (-2.98)* -0.0908 -0.0351 (-10.0)* (-3.44)* 0.1419 0.0811 535,462 138,355 0.1494 390,121 a 36.3* 14.2* 16.2* -2.29** -4.70* 1.18 -1.28 -0.54 0.10 2.77* 6.32* -4.97* -2.28** -1.99** -16.9* -12.3* -12.4* -6.89* -4.70* -1.17 -5.07* -0.06 -3.16* 0.37 -1.19 0.78 50.4* 3.95* -1.70 0.18 -0.07 -0.02 -0.11 -1.23 -0.48 -1.00 0.46 -4.09* 8.31* 237.6* 75.5* 76.0* 0.1271 145,341 Independent variables are defined in Table and country classifications are defined in Table White adjusted tstatistics are reported in parentheses b Common law countries are Australia, Canada, Hong Kong, India, Ireland, Malaysia, New Zealand, Singapore, South Africa, Thailand, United Kingdom, and the United States c Civil law countries are Argentina, Austria, Belgium, Denmark, Finland, France, Germany, Indonesia, Italy, Japan, South Korea, Mexico, Netherlands, Norway, Philippines, Portugal, Spain, Sweden, Switzerland, Taiwan, and Turkey d Amounts shown represent tests of differences in coefficient betweens the two groups e The Chow statistics test that the vectors of estimated coefficients are the same for the two groups *, ** Significant at p < 0.01, 0.05 38 TABLE Regression of analyst relative forecast accuracy on analyst- broker-specific characteristics in the Civil law countries, sample period: 1984-2001 Variablesa Intercept Pred Sign HORIZ + CHANGE -/? EXP - COMP + SPEC -/? FREQ - B-SIZE - B-IND - PIN + POUT + C-EXP - C-SPEC - B-C - Chow-F f R2 Obs French Originb -0.0025 (-0.60) 0.0025 (60.9)* 0.0011 (0.13) -0.0142 (-1.84) -0.0004 (-3.31)* 0.0138 (1.01) -0.0070 (-5.00)* -0.0011 (-3.40)* 0.1856 (3.19)* 0.0106 (0.73) 0.0877 (4.91)* 0.0078 (0.96) -0.0319 (-2.69)* -0.0613 (-4.99)* German Originc -0.0028 (-0.52) 0.0020 (41.6)* -0.0030 (-0.34) 0.0276 (3.45)* 0.0001 (0.97) 0.0208 (1.05) -0.0259 (-11.7)* 0.070 (1.74) 0.2544 (3.60)* 0.0955 (4.42)* 0.0811 (4.73)* 0.0097 (1.16) -0.0011 (-0.06) -0.1084 (-7.25)* Scand Origind -0.0085 (-1.02) 0.0025 (24.8)* 0.0304 (1.71) -0.0026 (-0.16) 0.0007 (1.90) -0.0679 (-2.42)* -0.0179 (-4.83)* -0.020 (0.32) 0.1253 (1.70) -0.0376 (-1.01) 0.1454 (3.10)* 0.0089 (0.51) -0.0176 (-0.70) -0.1100 (-3.10)* 0.0881 70,011 0.0711 52,951 0.0980 15,393 a French vs German t-stat.e 0.05 8.29* French vs Scand t-stat.e 0.65 German vs Scand t-stat.e 0.58 0.90 -4.28* -0.33 -1.49 -1.68 -3.76* -0.63 1.63 -2.61* -2.77* -1.40 -0.29 2.62* 7.23* 2.76* 2.59* -1.85 -3.55* -1.16 1.10 -0.75 0.64 1.27 3.26* 1.20 3.09* 0.27 -1.15 -1.29 -0.16 -0.02 0.01 -1.40 -0.51 0.53 2.44** 1.30 0.04 76.6* 49.8* 60.8* Independent variables are defined in Table White adjusted t-statistics are reported in parentheses Countries in the French origin are Argentina, Belgium, France, Indonesia, Italy, Mexico, Netherlands, Philippines, Portugal, Spain, and Turkey c Countries in the German origin are Austria, Germany, Japan, South Korea, Switzerland, and Taiwan d Countries in the Scandinavian origin are Denmark, Finland, Norway, and Sweden e Amounts shown represent tests of differences in coefficients between the two groups f The Chow statistics test that the vectors of estimated coefficients are the same for the two groups *, ** Significant at p < 0.01,